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Small and Medium-Sized Enterprises (SMEs): The Engine of Economic Growth through Investments and Innovation

by 1,* , 2 , 3 and 1

i

Department of Finance, The Bucharest University of Economic Studies, half-dozen Piata Romana, 010374 Bucharest, Romania

ii

Department of Information science, Statistics and Mathematics, Schoolhouse of Computer Scientific discipline for Business Management, Romanian-American Academy, 1B Expozitiei Blvd, 1st District, 012101 Bucharest, Romania

3

Doctoral School of Sociology, Academy of Bucharest, 36-46 Mihail Kogălniceanu Blvd, 050107 Bucharest, Romania

*

Author to whom correspondence should be addressed.

Received: sixteen November 2019 / Revised: sixteen December 2019 / Accustomed: 23 December 2019 / Published: ane January 2020

Abstract

Minor and medium-sized enterprises (SMEs) are crucial for local economic development, playing a noteworthy role in job creation, poverty alleviation and economic growth, just they encounter many funding barriers. The purpose of the current paper is to investigate the bear upon of investments and innovation on territorial economical growth, as measured by turnover, for Romanaian active enterprises, especially SMEs, over the menses 2009–2017. By estimating several log–log linear regressions, the quantitative outcomes provide support for a positive influence of investments on turnover. The clan was confirmed both for all agile enterprises at the national level, too as for micro, pocket-sized, center-sized and big companies. Every bit regards expenditures on innovation, a positive impact on turnover was best-selling for all enterprises and especially for big companies, just at that place was an absence of any statistically significant relation in the example of SMEs. The impact of firm size on turnover was positive for all agile enterprises at the national level, along with active micro-units. Besides, the estimation results show a positive impact of the number of active micro-units on territorial economical growth. The empirical findings are relevant to managers and policymakers in order to stimulate, encourage and offer back up to SMEs' evolution through their strategies.

ane. Introduction

Small and medium enterprises (SMEs) are a noteworthy driver of economic evolution [1], beingness vital to well-nigh economies across the world, particularly in developing and emerging nations [2]. They represent 99% of all businesses in the European Marriage (EU) and in the terminal five years, provided about 85% of new jobs, also ensuring 2-thirds of the total private sector date in the region [3]. For case, in 2015, at that place were most 23 1000000 SMEs that provided 90 1000000 jobs, generating a higher added value of 3.9 billion EUR [iv]. Dissimilar to big corporations, SMEs are highly flexible, revealing a superior flexibility to technical shifts, higher promotion of income distribution and improve adaptability to fluctuations in the market and new customer requirements, while their organizational construction allows for quicker decision making [5]. Nevertheless, to achieve this potential, SMEs need a continued source of longstanding funding so equally to invest in growth opportunities [6]. Hence, wishing to strengthen the entrepreneurial spirit in Europe and to create weather for the practical evolution of innovative concepts, the European Commission designed a set of measures alongside a modern and coherent policy for SMEs. The master purpose of this plan is to support the growth and development of SMEs in close relation to the employment market place [seven]. SMEs are viewed as the backbone of an economy [8] since they exert a pregnant function in lessening poverty, employment creation, strange trade promotion and technique innovation [9], also contributing meaningfully to the growth of developing economies [10]. And then, the strategy adopted in 2008 for Europe via the "Small Business Act" considers the awarding of the principle "Offset, call back at a small scale" [11] regarding the adoption of policies, regulations and policy measures that should offer support to the needs of SMEs. Since 2010, the European Parliament has adopted a series of resolutions, such every bit: community policy to stimulate innovation through European SMEs [12], industrial policy for globalization conditions [thirteen], the competitiveness event and the issue of business organisation opportunities in the European Union [14], the re-industrialization of the economy to provide competitiveness and sustainability [15], supporting information and communications applied science (ICT) evolution regarding the transition towards a blazon of sustainable economic system and the resolution of social and ecological problems mentioned in the European Strategy 2020 [16], easy access for SMEs towards financing possibilities [17] and the stimulation of research/development and innovation processes offered by the research plan Horizon 2020 [18,19]. Thus, through European and national policies, and through start-up financing programs or business organization incubators, SMEs have been encouraged and stimulated to develop, grow and support the economy and local, regional and national activities inside communities.

Since markets have go gradually more competitive, SMEs effort to affirm themselves via new product advances in order to compete with large enterprises. Hence, innovation plays a fundamental role in accomplishing competitive power [20], beingness oriented towards novel products, original marketing and management methods, too as genuine technologies [21]. The model of Porter [22,23] promotes the thought that enterprises vest to a greater globalization and internationalization procedure. Competitive advantage is created and supported locally within a national or regional surface area, each having its own economical or cultural characteristics. The European Commission defines a competitive region as i that is able to "ensure both the number and the quality of jobs" [24]. Regarding this description, the gross domestic product (GDP)/inhabitant is considered to be a proper representation. It tin can exist deconstructed into more components with precise economic interpretations [25].

The idiom "regional competitiveness" is a phrase that refers neither to macroeconomy nor to microeconomy because regions can be considered neither simple accumulations of enterprises from a sure area, nor reductions of a certain nation to a smaller calibration. Therefore, it tin exist considered that the regional level is much more complex and challenging to analyze. Regions are in direct competition with each other for the conquest of markets and for alluring investments [26]. Hence, 1 region may possess absolute competitive advantages if these include technological and social factors concerning infrastructure or superior institutional factors compared with other regions, which are external to enterprises, but which contribute to their evolution and greater success.

This paper aims to investigate the role of agile enterprises, peculiarly SMEs, in territorial development in Romania, every bit measured by their turnover. Previous studies on SMEs' access to finance and their innovative capacity have focused on various states such as Republic of albania [27], Brazil [28], Poland [29,30,31,32], Romania [21,33,34,35,36,37,38], Spain [5,39], Malaysia [40], Eye Eastern and Central Asian states [41], Nigeria [1], Portugal [42], United States [43], Vietnam [44] and Republic of zimbabwe [45]. Nevertheless, preceding research for the Romanian example has focused on topics such as the involvement of SMEs in economic growth [38], the function of SMEs in improving employment [37], SMEs' financing options [36], the influence of globalization on SMEs [35], the sustainability of SMEs [33] as well every bit their importance [34] or innovative capacity [21]. Our research contributes to the existing literature in a number of means. First, we focus on SMEs in the context of an upper-middle-income nation. The fruitful development of central and eastern European nations from planned economies to market place-oriented economies would not have been feasible without the enlarged number of SMEs [xxx]. Studies of this type are rare due to the predilection for exploring firms that are larger, more productive and more than capital intensive [44]. However, another particularity is that well-nigh developing markets feature a depository financial institution-based formal financial system with frail disinterestedness and debt markets [46], alongside a lack of innovation management feel and state-of-the-art engineering [47]. Due to the inheritances of central planning, the growth of SMEs is severely inhibited by their restricted admission to finance [48]. 2nd, the newspaper advances the literature by providing empirical bear witness for the affect of investments and expenditures related to innovation on territorial development in Romania, which to the best of our noesis has not been explored previously.

The paper is structured every bit follows. Section two examines the previous literature and establishes the research hypotheses, while Section 3 is dedicated to the empirical framework. Section 4 outlines and discusses the quantitative outcomes. The last section provides final remarks and policy implications.

two. Theoretical Backgrounds, Literature Review and Hypotheses Evolution

2.1. Prior Research on SMEs Financing

SMEs play an ever more imperative function towards market evolution locally and overseas, influencing sustainable growth in the trading, production and service areas via attracting investments [31]. Co-ordinate to Kersten, et al. [49], SMEs funding plans reveals a positive significant effect on the total volume of financing and/or investment, firm performance and employment. With reference to the theories that enlighten the factors driving business firm investment, the acceleration principle theory [50] postulates that companies increment their investments during satisfactory stages of economical growth, whilst investments are reduced during economic recession. According to neoclassical theory [51], firms increase investment if sales increase, merely investment diminishes if sales fall.

SMEs are oriented towards profit maximization rather than expansion since their legal status is usually proprietorship or partnership, these companies existence ruled by the resolutions of owners. Owners employ their own assets to fund their businesses, because profits more than vital than investments [52]. Wellalage and Fernandez [46] documented that having formal finance is positively related to firm-level product innovation and procedure innovation. Nevertheless, SMEs are challenged with serious financial limitations when compared to listed firms since they cannot increase funds by issuing stocks and bonds, thus bank borrowing is the core source of funding [53]. Besides, scarce collateral, weak solvency, short/no credit history, young bank-borrower dealings, high transaction costs and information asymmetry were noticed as the chief difficulties with reference to gathering commercial bank financing, peculiarly long-term borrowings [43]. Wang [54] exhibited the most significant impediments observed past SMEs managers, namely "access to finance", "tax rate", "competition", "electricity" and "political factors".

Ullah [48] noticed that SMEs encounter lesser financial restraints in nations with higher levels of Gdp per capita, stock market place development, legal systems and property rights, as well reduced levels of corruption. Motta and Sharma [28] confirmed that firm size may affect admission to capital in the hospitality SME sector as small-scale-sized companies may not own high-quality projects required to acquire banking company credits from financial intermediaries. In this vein, Jackowicz and Kozlowski [32] proposed that social ties amongst SMEs managers and bank employees may enhance SMEs' access to banking company financing and rouse their investments.

Hypothesis1a.

The turnover of all agile enterprises at the national level is determined by the investments performed past these firms and their numbers of employees.

Hypothesis1b.

The turnover of all agile companies at the national level is driven by the gross investments performed past these enterprises.

Hypothesis1c.

The turnover of all active firms at the national level is determined by the gross investments performed by these companies, investigated according to business size.

Hypothesis1d.

The turnover of all the micro-enterprises at the national level is driven past the net investments performed past these companies, the number of active micro-units and the number of employees.

2.2. Previous Studies on Innovation in SMEs

In a globalized era—a society based on cognition, respectively—the SMEs reveal exclusive competitive returns on the force of flexibility and quick adaptation to the new requirements of production and global market, exposure to new industrial know-hows [55,56], the ability to place and to apply the value of novel external information [57], the power to chop-chop adopt the modern techniques and technologies for new business models [58], the rapid decision and the lack of bureaucracy in the process of implementing new products and innovative processes [59]. As such, a crucial part in shaping a competitive benefit is revealed by innovation [29]. Exposito and Sanchis-Llopis [39] noticed that innovation of any type (product, procedure and/or organizational) positively influences financial and operational extents of SME operation. Besides, The Organization for Economic Co-operation and Development (OECD) [60] reinforced that college levels of firms' investment in innovation determine higher innovation sales and productivity.

The competitiveness regarding a company refers to "the chapters of producing proper appurtenances and services of eligible quality, at the right price and at the right time" [61] or "the chapters of companies to compete, to develop and to increase profits" [62]. Besides, competitiveness is viewed equally the "dynamic part that depends on progress, innovation and on the ability to self-alter and improve" [23]. Through his pattern, Porter [23] reinforces the thought that, even if the product of globalization takes place forth with the commercial changes, the competitive reward is created and supported through a local process. The national or regional area is the one which, through its economic, cultural or institutional propriety, allows the development of certain economical activities, of specific undergrowth or branches of activity or not.

Along with the industrial development and the improvement in the knowledge social club, the usage of knowledge had get the new economic of import resources, which changes the approach towards operation and competitiveness completely. The company's resources back up its competitive advantage [63], and the manager must nowadays analyze the production along with its social concerns, labor conditions and the value of the local consumption [64]. A new capability of the entrepreneur regarding the concern success is emphasized, regarding the social competence [65], and the advice mode with the providers and the clients, the promoting strategy and the usage of proper visual symbols [66].

The practice strategy has proven that inside ane company competitiveness often requires a long learning procedure which, at the local level tin can be attained through a common effort of all the universities, inquiry institutions, investors and entrepreneurs past creating and developing certain powerful networks [67] with the purpose of reinforcing some new technologies and by creating some business incubators [68] that might back up the local innovation process. All the same, competitiveness will exist emphasized not but by quality and performance, simply also by the plentiful processes inside the company. Amongst these types of processes, the managerial ones are really imperative. Thus, there are studies [69] that prove that the management of SMEs, having solid positions on the marketplace, usually make more than bourgeois choices of disengagement resources, in order to preserve and strengthen the actual turn a profit. The experience accumulated on the market place and the market feedback is required to build a strategy appropriate to local conditions.

Innovation represents the process of placing a certain production on the marketplace (goods or services), a new ane or a significantly improved one. Innovation focuses on the cooperation between research and industry, due to the demand for finalizing the research through practical results related to the technical and technological developments. In Romania, innovation is divers [lxx] as being both a process and a product: "innovation—every bit a product—represents a new function, or the comeback or a broader function of a certain product, process or service, in any domain, which can or could exist available to the demand on the market place, which may or might generate a new type of market; innovation—every bit a procedure—represents the activeness that allows the occurrence of innovation. Innovation—as a process—includes the connectedness between research and development". To all the to a higher place mentioned, typically, the marketing innovation and the organizational innovation can exist added, offering extremely more development opportunities, that allow the usage of high technologies and modern advisory technologies for structure and management. Besides, OECD [71] defines iv types of innovation, namely "product innovation—the introduction of a proficient or service that is new or significantly improved with respect to its characteristics or intended uses; process innovation—the implementation of a new or significantly improved production or commitment method; marketing innovation—the implementation of a new marketing method involving pregnant changes in product design or packaging, production placement, production promotion or pricing; organizational innovation—the implementation of a new organizational method in the business firm's business practices, workplace organization or external relations". Saridakis, et al. [72] differentiate amid radical innovation defined as progression in knowledge due to the advance of novel products and processes that are new to the market/industry and incremental innovation defined equally an unremitting enhancement to products, processes or services that are novel to the firm just.

The practice has shown that the innovation process of a certain product, service or new performant technology is inseparably connected to intense research-development activity inside companies, among universities, inquiry institutions and other research entities, experts or scientists within all domains. By usually working on partnerships for different well-defined projects, implemented and monitored, these communities include the best professionals in the domain, man resources with high qualifications and outcomes in the fields, integrated into real scientific communities and able to reach the best performance. Globalization, the virtual, extremely dynamic marketplace, the technological development, the computerization and the quick digitization determine the involvement of these communities even more within economy, social and cultural life at the national and regional level. This is the only mode to withdraw the major differences between nations and regions and amid companies. As such, the EU determines the general policy, accepted by all the participating countries, of allocating the amount of 3% of GDP for the activities performed in the inquiry and development field. The national budgets allocated to research are distributed on research subjects with well-divers results which are monitored along the entire process. Among all these, the nearly important ones are the entrepreneurial and management culture, their capacities of identifying innovation opportunities [73], to work on a project basis and to identify, diminish or remove the potential risks of the projects [74].

The research and innovation activities include, equally a direct result, the increment of jobs, an economic increment and an increase in the quality of life. The new technologies support the new approach to social problems such as poverty, poor health and environmental deposition or work rubber, since SMEs are supported by a series of national or regional regulations [75], specially designed as political and social run a risk management strategies.

Consequently, the second prepare of hypotheses are explored:

Hypothesis2a.

The turnover of all active enterprises at the national level is determined by the expenditures on innovation performed past SMEs and big enterprises.

Hypothesis2b.

The turnover of all active enterprises at the national level is driven by expenditures on innovation performed by all enterprises.

Hypothesis2c.

The turnover of all active enterprises at the national level is adamant by the expenditures on innovation performed by all SMEs.

iii. Data and Methodology

3.1. Sample Description and Variables

The research sample consisted of business statistics-series–business organisation-demography, over the menstruum 2009–2017, the data source shown by the Romanaian Statistical Yearbook. The selected variables are presented in Table 1.

Consistent with prior studies [2,half-dozen,twenty,32,39,42,48,72], turnover was selected in lodge to take hold of the territorial economic growth, alongside investments [32], firm size [6,28,48,52,72], expenditures on innovation [47,72] and the number of enterprises.

3.two. Econometric Framework

With the purpose to examine the expressed hypotheses, similar to previous studies [2,32,53], we judge ordinary to the lowest degree squares (OLS) regressions. The log–log form is adopted, with MacKinnon–White (HC2) heteroskedasticity-consistent standard errors and covariance. In lodge to ensure the aforementioned society of magnitude, the logarithmic transformation was accomplished for all the selected variables, except the net investments performed past micro-enterprises at the national level (X1B2) due to several zero values. There will be estimated regression equations for each of the settled hypotheses, every bit depicted below.

Model 1 (M1). TY1A is the selected dependent variable that signifies the turnover of all active enterprises at the national level. The independent variables are TX1A1 representing the total gross investments acquired by these companies, alongside TX3A signifying the number of employees. A model of linear regression is built with 2 independent variables, β1, βtwo and βthree representing the parameters of the regression model and εi the disturbance term. The equation of the multiple linear regression is depicted below:

log(TY1A) = β1 + β2*log(TX1A1) + βthree*log(TX3A) + εi.

Model two (M2). TY1A is the chosen predicted variable that depicts the turnover of all the companies at the national level. The explanatory variable is TX1A1 representing the total gross investments performed by these companies. A fix of information with 64 values from 8 regions of development for a catamenia of eight years (2009–2016) is considered. A model of linear regression with a single independent variable is built, where β1 and β2 are the parameters of the regression model and εi the error term. The equation of the model of the univariate linear regression is described beneath:

log(TY1A) = C(1) + βii*log(TX1A1) + εi.

Model iii (M3). TY1A is the elected explained variable that shows the turnover of all the companies at the national level. The predictor variables are TX1B1, TX1C1, TX1D1 and TX1E1 showing the total gross investments performed past the micro, minor, centre-sized and big companies. A model of linear regression with four independent variables is estimated, βone, β2, β3, β4 and β5 existence the parameters of the regression model and εi the error term. The equation of the model of multiple linear regression is shown underneath:

log(TY1A) = βane + β2*log(TX1B1) + βiii*log(TX1C1) + β4*log(TX1D1) + βfive*log(TX1E1) + εi.

Model iv (M4). Y1B is the selected dependent variable that represents the turnover of all the micro-enterprises at the national level. The regressors are X1B2 depicting the net investments performed by these companies, X2B representing the number of agile micro-units and X3B measuring the number of employees. The data serial includes 832 values, respective to the 8 regions of development, regarding 8 years and thirteen sectors of the economy. A model of linear regression is estimated, having 3 independent variables, where βone, βtwo, β3 and β4 depict the parameters of the regression model and εi the disturbance term. The equation of the multiple linear regression model is described below:

log(Y1B) = β1 + β2*log(X1B2) + β3*log(X2B) + βiv*log(X3B) + εi.

Model 5 (M5). Information technology is considered that the turnover at the national level is adamant by the expenditures on innovation performed by companies. It is analyzed based on two size categories: SMEs and large enterprises. TY1A is the chosen regress and that represents the turnover of all the companies at the national level. The independent variables are TX4D and TX4E that catches the expenditures on innovation performed by SMEs and large enterprises. The data serial includes 24 values, respective to the 8 development regions for three years. Yet, in the Romanian Statistical Yearbook, the data regarding expenditures on innovation are reported every two years. A model of linear regression with two contained variables is estimated, βi, βii and β3 showing the parameters of the regression model and εi the disturbance term. The equation of the model of multiple linear regression is represented below:

log(TY1A) = βane + β2*log(TX4E) + β3*log(TX4D) + εi.

Model 6 (M6). It is considered that the turnover at the national level is driven by the total expenditures accomplished by enterprises. TY1A is established as the dependent variable that signifies the turnover of all the companies at the national level. The independent variable is represented by TX4A that measures the expenditures on innovation performed by all companies. A model of linear regression with a single contained variable is estimated, where βone and βii are the parameters of the regression model and εi the error term. The equation of the model of simple linear regression is depicted underneath:

log(TY1A) = βone + βtwo*log(TX4A) + εi.

Model 7 (M7). It is considered that the turnover at the national level is determined by the expenditures on innovation performed by companies. TY1A is acknowledged equally the response variable that catches the turnover of all the companies at the national level. The explanatory variable is TX4E that measures the expenditures on innovation performed by all SMEs. A model of elementary linear regression with a single independent variable is estimated, where β1 and β2 are the parameters of the regression model and εi the disturbance term. The equation of the model of simple linear regression is defined underneath:

log(TY1A) = β1 + βtwo*log(TX4E) + εi.

4. Empirical Findings and Discussion

4.1. Preliminary Assay

The data regarding the total turnover of Romanian enterprises is analyzed further. Tabular array 2 concerns the local active units from industry, structure, merchandise and other services from the eight Romanian development regions.

In line with Table ii and Figure 1, we notice that for region number six, Bucharest–Ilfov, the ane that includes the upper-case letter and the neighboring areas, at that place is a superior development level compared to other areas. This may occur probably due to the more attractive business organisation environment, offering both opportunities for new companies and qualified, trained personnel with bully experience in management, amend access to diverse resources, infrastructure, specialists and links with academics.

The annual turnover for big, active companies at the national level, and the ones accomplished past SMEs, is reported in Table three, whereas Figure 2 reveals that the SMEs' full turnover exceeds the turnover of large enterprises.

Thus, it is proven that in that location is a greater importance that needs to exist granted to the support and evolution of SMEs, as a pillar of the territorial development, through the governmental, European or regional policies belonging to the local and central administrative policymakers.

By analyzing the turnover of enterprises of different sizes, as conveyed in Table 4, fifty-fifty if all of them experienced the same upwardly trajectory, Effigy 3 reveals that the large enterprises have a greater value for this indicator. Nevertheless, all of these cumulated information indicate that SMEs may greatly exceed this value (Figure 2).

By exploring the expenditures on innovation performed by SMEs and big enterprises in Romania, as showed in Tabular array 5 and Effigy 4, it is noted that, at the national level, the innovation spending related to SMEs follows the increased tendency of big enterprises, even the absolute value is smaller than the one of the big companies. A closer analysis of years and regions of evolution, reported in Tabular array 6 and Figure 5, shows that in Romania, the total innovation of SMEs from the Bucharest–Ilfov region is larger than the innovation registered in the other regions. Thus, it is confirmed that an attractive region, such equally the uppercase city of a state, offers suitable transport infrastructure, business concern opportunities, experience and improve collaboration with the academic and research community, human resource of diverse specializations and a competitive and challenging environment. The innovation of SMEs depends to a large extent on the specificity of the company [77], in close relation to the company's position on the market place and its connection to the market's request. It as well hinges on its organizational strategy and its direction, on the protection offered for different measures of prevention and protection against the risks that may occur especially in the field of investment.

Prior studies show that most of these problems may be recognized and solved through an extensive-learning organizational process. Its features are the capacity to successfully influence the organizational performance of SMEs [78,79], the ability to pb on a competitive advantage [eighty] to a strong performance of the concern model [81] mirrored through the turnover, as well equally to foster social responsibility measures along with development and competitive growth [82]. The organizational capacity of learning may manifest itself via certain actual mechanisms, such every bit experimentation, risk-taking, interaction with the external environment, the dialogue and the participatory decision-making process [83]. It tin can likewise manifest itself through adopting modern high-performance technologies [84] proving the importance of learning over the study of the market [85] and of the clients' choices on a very dynamic actual market [86]. These all contribute to the valuable effect on the performance of SMEs regarding the ecological dynamism [87], of light-green products [88] and of the investments regarding the training and specialization of the human being resource [89].

SMEs' development and their orientation in an extremely dynamic global market are reliant on the educational and continuous preparation process of human resources for new specializations, that may ensure the usage of new high-performance technologies in conditions of high functioning for enterprises [90]. The two atmospheric condition are essentially related as follows: the man resource training limits the usage of new technologies in the field for active companies and even for their management [91]. In response, all these volition create certain conditions regarding the performed investments and innovations of the management teams in general, and of the SMEs in item. The SMEs with a lower charge per unit of bureaucracy, with a greater flexibility and a more rapid capability to adapt to the market requests, may grow, may develop more efficiently through investments and innovation expenditures, in all its forms.

iv.2. Regression Models Outcomes

4.2.1. The Impact of Investments on Territorial Economic Growth

The empirical outcomes of the first regression model are reported in Table 7. The estimated value of the coefficient of determination (R-squared) reveals that the variation of the values of the independent variables (TX1A1 and TX3A) explains the variation of the dependent variable (TY1A) on a ratio of 90.59%. For each i% increase in the value of gross investments (TX1A1), turnover (TY1A) increases past 0.25%. Therefore, the neoclassical theory [51] is supported. Likewise, if the number of employees (TX3A) increases by one per centum, turnover will increase past 1.11%, as in prior studies [6,28]. Hence, Hypothesis 1a is confirmed, the turnover at the national level being determined by the investments performed by companies and past the number of employees.

With reference to the second hypothesis, the empirical estimations are shown in Tabular array 8. The estimated value of the R-squared reveals the fact that the variation of the values of the independent variable (TX1A1) explains the variation of the dependent variable to an extent of 79.69%. If the value of gross investments (TX1A1) rises by i percentage, turnover will increment by 0.77%. Consistent with the results from Table 7, the investments volition determine an intensification of the profitability, consistent with [49]. Thereby, Hypothesis 1b is confirmed, the turnover at the national level being determined by the investments performed by companies.

Further, the enquiry aims to investigate to what extent the turnover of all the companies at the national level is driven by the investments performed by companies, depending on their size (micro, small, medium and big). As regards the third model, the quantitative results are displayed in Tabular array 9.

The estimated value of the R-squared reveals the fact that the simultaneous variation of the values of the independent variables (TX1B1, TX1C1, TX1D1 and TX1E1) explains the variation of the dependent variable (TY1A) to an extent of 92.92%. If the value of investments performed by micro-enterprises (TX1B1), small companies (TX1C1), middle-sized companies (TX1D1) or big companies (TX1E1) increases past one percent, turnover will increase past 0.21%, 0.37%, 0.14% or 0.11%. Hence, Hypothesis 1c is confirmed, the turnover being determined past the investments performed by companies for all types of sizes. The prominence of the SMEs over the evolution and support of the economical growth of a certain land is proved. This reputation is best-selling by EU legislation and regulations, through the government policies that promote the establishing, increase and financing process of SMEs. The consideration of political factors must exist fatigued so as to ensure like shooting fish in a barrel admission of these enterprises to financing resources [17], alongside better and diverse development opportunities. Likewise, SMEs that are flexible due to their size, organization and dynamics are easily adjustable to the market place request. They must wisely adapt their investments to a high implementation level of the technology and to the automation process of all the activities, to ensure work and environment conditions according to modernistic standards, besides the products and processes innovation. All these are attracted and implemented by a modern management that uses techniques, procedures and up-to-appointment programming and command instruments which would ensure the beingness and the evolution of the long-term of the SMEs, in the context of a continuously fluctuating market.

The estimations related to the fourth econometric model are conveyed in Table 10. The estimated value of the R-squared reveals the fact that the variation of the values of the contained variables (X1B2, X2B and X3B) explains the variation of the dependent variable (Y1B) to an extent of 75.92%.

For each i% increase in the number of micro-units (X2B) and the number of employees in a micro-enterprise (X3B), turnover (TY1A) increases past 0.35% and 0.51%, respectively. A one-unit of measurement increase in net investments performed by micro-enterprises (X1B2) will generate an increase in the geometric mean of turnover by 0.06%. Nevertheless, for minor firms, the sales revenues are regularly more appropriate in overcoming liquidity problems than for funding the investment [42]. As such, Hypothesis 1d is confirmed, the turnover of micro-enterprises at the national level being determined by the investments performed past companies, by the number of micro active units and by the number of the employees. All the same, with reference to this category of enterprises, the investments are fifty-fifty more difficult to be implemented because the visitor should access external financing resources.

four.2.2. The Impact of Innovation on Territorial Economic Growth

The outcomes of the fifth regression model are exhibited in Table 11. The estimated value of the R-squared reveals the fact that the simultaneous variation of the values of the independent variables (TX4E and TX4D) explains, to an extent of 59.21%, the variation of the dependent variable (TY1A). For each 1% increment in the value of expenditures on innovation performed by big companies (TX4E), turnover (TY1A) increases by 0.37%. Besides, in the example of SMEs, the relationship between expenditures on innovation and turnover is not statistically pregnant. Unfortunately, in line with Armeanu, Istudor and Lache [38], Romanian SMEs reveal a reduced ability to innovate, aslope their concentration in depression-value-added generating areas, restricted access to financing and poor management. Therefore, Hypothesis 2a is confirmed but for SMEs.

The results regarding the examination of the sixth hypothesis are presented in Tabular array 12. The estimated value of the R-squared reveals the fact that the variation of the contained variable (TX4A) explains, to an extent of 39.01%, the variation of the dependent variable (TY1A). Also, if the value of expenditures on innovation performed by all enterprises increases by one pct, turnover will increment by 0.26%. Consequent with Exposito and Sanchis-Llopis [39], the introduction of product innovation emphasizes a positive bear on on the probability of sales intensification. Hence, Hypothesis 2b is confirmed.

The outcomes of the seventh model are reported in Table 13. The estimated value of the R-squared reveals the fact that the variation of the independent variable (TX4E) explains, to an extent of 57.44%, the variation of the dependent variable (TY1A). Hence, for each ane% increment in the expenditures on innovation performed past big enterprises (TX4E), turnover (TY1A) increases by 0.31%. Hereby, Hypothesis 2c is confirmed. Companies invest in innovation aiming to gather market share, subtract costs and plow out to be more prolific. Since customer requests have become more than specific and competition has amplified, innovation is essential [60].

5. Concluding Remarks and Policy Implications

The electric current study explored the affect of investments and innovation on territorial economic growth, as measured past the turnover, for Romanian business concern statistics serial over the period 2009–2017. By estimating several log–log linear regressions, the empirical findings provide support for a positive impact of investments on territorial economic growth. The relationship was proven both for all active enterprises at the national level and for micro, small, heart and big companies. In the case of innovation-oriented expenditures, our interpretation results testify a positive impact on turnover for all enterprises and large companies, simply a lack of association was established in the case of SMEs. Besides, there was a noticeable positive impact of business firm size on turnover for all active enterprises at the national level, as well every bit for active micro-units. Likewise, the results suggested a positive influence on the number of active micro-units on territorial economic growth.

In our view, this written report has some noteworthy policy implications. The managers should learn from the previous experience of other companies whose success or failure had been investigated so every bit to recognize the factors and relations that led to factual results [92]. These companies will accept to adopt several requirements such equally: A willingness to give up the traditional blazon of management, possible, by identifying and accepting new challenges, building an organizational construction regarding the learning procedure and the adoption process of new technologies along with the innovation involved inside all domains. All these essentially atomic number 82 to management innovation, a segment that is directly related to the organizational facet of the company [93], to the usage of new managerial practices, the practice of so-called management innovators [94]. They should try to combine the ideas gained from diverse studies, analyze and further adapt them to the specific needs of the intended enterprises. All these together class the organizational innovation and carry an of import role regarding the results of innovation [95] aiming at the process and the last products.

In Romania a series of programs of European financial support is bachelor to exist accessed, by SMEs, through certain projects regarding development, the implementation of loftier technologies or the mechanization of some processes, for the educational activity of employees, for the protection of the surround, for the quality assurance in all domains, to provide support for the management efficiency [96]. There are also other types of projects that help the SMEs by offering financing, funding business incubators, stimulating some activities or encourage higher employment, motivating the purchase and the implementation concerning information and communication technologies that grade the cadre of evolution. It is vital to have a bigger fiscal arrangement that is able to support entrepreneurs and the dissimilar stages of the lifecycle of small firms [46]. College levels of economic, financial and institutional expansion are imperative in improving house fiscal restrictions [48].

This study has several restrictions. Possibly the most constraining limitation of this research is emphasized by the selected dataset, collected from the Romanaian Statistical Yearbook. Owning more disaggregated information, namely firm-level data, would allow catching more characteristics such as total assets, house age, labor productivity or internationalization. Given that the heterogeneous aspect of SMEs hinges on the size of a region's economic activity, regional effects intended for decision-making for the viii regions of Romania were not considered. Besides, consistent with Motta and Sharma [28], employing the number of employees as a mensurate towards house size may be challenging inasmuch as the number of employees is driven by the economic size of the company, quality of the firm, option of operational leverage and type of business. As future enquiry avenues, the electric current paper should be developed by exploring the causal relationships between, investments, innovation and territorial economic growth. As well, a sectorial assay may depict another forthcoming direction.

Author Contributions

Conceptualization, Ș.C.G., G.A.B., A.H. and L.Northward.S.; data curation, Ș.C.Thousand., G.A.B., A.H. and L.N.Due south.; formal assay, Ș.C.One thousand., M.A.B., A.H. and L.N.S.; funding conquering, Ș.C.G., M.A.B., A.H. and L.N.S.; investigation, Ș.C.G., K.A.B., A.H. and 50.N.Due south.; methodology, Ș.C.G., Chiliad.A.B., A.H. and Fifty.North.Southward.; project administration, Ș.C.G., M.A.B., A.H. and L.N.S.; resources, Ș.C.M., Grand.A.B., A.H. and 50.N.S.; software, Ș.C.Thou., M.A.B., A.H. and L.Northward.Southward.; supervision, Ș.C.G., Chiliad.A.B., A.H. and L.Northward.S.; validation, Ș.C.Yard., M.A.B., A.H. and L.Northward.S.; visualization, Ș.C.Thousand., M.A.B., A.H. and L.N.South.; writing—original draft, Ș.C.Chiliad., M.A.B., A.H. and L.N.S.; Writing—review and editing, Ș.C.Chiliad., M.A.B., A.H. and L.N.S. All authors have read and agreed to the published version of the manuscript.

Funding

This inquiry received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The total turnover of the offset companies from the eight developed regions. Source: Authors' processing from the Romanian Annual Statistical Yearbook 2010–2017 [76].

Figure 1. The total turnover of the first companies from the eight developed regions. Source: Authors' processing from the Romanian Annual Statistical Yearbook 2010–2017 [76].

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Figure ii. The turnover of SMEs (0–249 employees) and of big enterprises. Source: Authors' processing from the Romanaian Annual Statistical Yearbook 2010–2017 [76].

Figure two. The turnover of SMEs (0–249 employees) and of large enterprises. Source: Authors' processing from the Romanian Annual Statistical Yearbook 2010–2017 [76].

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Figure 3. Turnover of enterprises regarding the size category. Source: Authors' processing from the Romanian Almanac Statistical Yearbook 2010–2017 [76].

Figure iii. Turnover of enterprises regarding the size category. Source: Authors' processing from the Romanian Annual Statistical Yearbook 2010–2017 [76].

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Effigy 4. The value of expenditures on the innovation of SMEs and big enterprises. Source: Authors' processing from the Romanian Annual Statistical Yearbook 2010–2017 [76].

Effigy 4. The value of expenditures on the innovation of SMEs and big enterprises. Source: Authors' processing from the Romanian Almanac Statistical Yearbook 2010–2017 [76].

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Figure 5. Total expenditures on innovation for the enterprises in the eight regions of development. Source: Authors' processing from the Romanian Almanac Statistical Yearbook 2010–2017 [76].

Figure 5. Total expenditures on innovation for the enterprises in the eight regions of development. Source: Authors' processing from the Romanian Almanac Statistical Yearbook 2010–2017 [76].

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Table ane. Variables' description.

Table i. Variables' description.

Variables Definitions
Variables regarding business turnover (Dependent Variables)
TY1A The turnover of all active enterprises at the national level
Y1B The turnover of all the micro-enterprises at the national level
Variables regarding investments (Explanatory Variables)
TX1A1 Total gross investments obtained by all active enterprises at the national level
TX1B1 Full gross investments performed by the micro companies
TX1C1 Total gross investments performed by the small companies
TX1D1 Total gross investments performed by the heart-sized companies
TX1E1 Full gross investments performed by the big companies
X1B2 Net investments performed by micro-enterprises
Variables regarding expenditures on innovation (Explanatory Variables)
TX4A Expenditures on innovation performed by all enterprises
TX4D Expenditures on innovation performed by SMEs
TX4E Expenditures on innovation performed past big enterprises
Variables regarding firm size (Explanatory Variables)
TX3A The number of employees out of all active enterprises at the national level
X3B The number of employees out of active micro-units
Variables regarding the number of enterprises
X2B The number of agile micro-units

Table 2. Total turnover for the active local units from manufacture, construction, trade and other services.

Table two. Total turnover for the active local units from manufacture, construction, merchandise and other services.

Region 2009 2010 2011 2012 2013 2014 2015 2016
ane North-West 85,673 91,222 101,068 106,958 10,8911 115,831 124,021 134,251
2 Center 88,253 95,354 106,679 114,973 119,574 126,904 133,324 143,313
3 North-East threescore,414 61,355 67,979 75,897 75,251 77,896 84,512 89,131
4 South-Due east 86,943 93,476 103,380 115,446 106,792 106,701 110,370 106,377
five South-Muntenia 97,848 107,136 128,467 132,425 130,652 137,503 138,421 145,959
6 Bucharest–Ilfov 316,201 332,956 360,042 363,868 363,665 381,902 415,115 430,203
7 South-W Oltenia 49,527 50,953 57,426 61,734 59,110 61,177 64,697 66,159
8 Westward 65,975 70,786 82,110 89,207 91,037 97,804 106,706 117,095

Tabular array 3. The turnover of active companies, minor and medium enterprises (SMEs) and large enterprises (mil Lei).

Table iii. The turnover of active companies, minor and medium enterprises (SMEs) and big enterprises (mil Lei).

Year/No. of Employees 0–9 x–49 fifty–249 >250
2009 172,783 209,784 226,239 242,028
2010 178,450 221,111 236,806 266,871
2011 193,513 241,214 264,461 307,963
2012 212,861 264,998 267,928 314,721
2013 200,138 258,106 282,285 314,464
2014 212,025 276,365 287,391 329,936
2015 217,517 295,635 314,397 349,618
2016 220,570 303,257 337,667 370,994

Table 4. The turnover of active companies and the size categories (mil Lei).

Table 4. The turnover of active companies and the size categories (mil Lei).

Year SMEs (0–249) Big Companies (>250)
2009 608,806 242,028
2010 636,367 266,871
2011 699,188 307,963
2012 745,787 314,721
2013 740,529 314,464
2014 775,781 329,936
2015 827,549 349,618
2016 861,494 370,994

Tabular array 5. Expenditures on innovation of SMEs and big enterprises (mil Lei).

Table 5. Expenditures on innovation of SMEs and big enterprises (mil Lei).

Year Big SMEs
2012 1,663,447 ane,253,844
2014 ii,068,974 ane,369,763
2016 three,732,421 2,623,607

Table vi. Expenditures on innovation within active enterprises depending on regions (mil Lei).

Tabular array half-dozen. Expenditures on innovation within active enterprises depending on regions (mil Lei).

Region 2012 2014 2016
ane North-Westward 251,952 111,085 82,354
two Middle 401,308 353,020 496,385
three Due north-East 188,403 287,744 66,920
four South-Eastward 686,340 490,651 284,162
5 South-Muntenia 277,680 628,358 283,633
6 Bucharest–Ilfov 861,600 ane,145,411 537,984
7 S-West Oltenia 123,233 xv,789 4912
8 West 126,775 406,679 57,815

Tabular array 7. Estimated parameters of the multiple linear regression model M1 by using the method of least squares.

Table 7. Estimated parameters of the multiple linear regression model M1 by using the method of least squares.

Variable Coefficient Std. Error t-Statistic Prob.
C −five.198040 1.723701 −iii.015628 0.0037
log(TX1A1) 0.254622 0.135583 one.877976 0.0652
log(TX3A) 1.105328 0.225105 4.910274 0.0000
R-squared 0.905874 Hateful dependent var xi.61481
Adjusted R-squared 0.902788 Southward.D. dependent var 0.532504
S.E. of regression 0.166028 Akaike info criterion −0.707575
Sum squared resid i.681492 Schwarz criterion −0.606377
Log likelihood 25.64239 Hannan–Quinn criter. −0.667708
F-statistic 293.5348 Durbin–Watson stat 1.015572
Prob(F-statistic) 0.000000 Wald F-statistic 648.6972
Prob(Wald F-statistic) 0.000000

Tabular array 8. Estimated parameters of the simple linear regression model M2 past using the method of to the lowest degree squares.

Table 8. Estimated parameters of the simple linear regression model M2 by using the method of least squares.

Variable Coefficient Std. Mistake t-Statistic Prob.
C 4.452171 0.683106 half dozen.517543 0.0000
log(TX1A1) 0.768108 0.074703 x.28216 0.0000
R-squared 0.796887 Hateful dependent var 11.61481
Adjusted R-squared 0.793611 S.D. dependent var 0.532504
South.Eastward. of regression 0.241917 Akaike info criterion 0.030305
Sum squared resid 3.628470 Schwarz benchmark 0.097770
Log likelihood ane.030236 Hannan–Quinn criter. 0.056883
F-statistic 243.2494 Durbin–Watson stat 1.441618
Prob(F-statistic) 0.000000 Wald F-statistic 105.7228
Prob(Wald F-statistic) 0.000000

Table ix. Estimated parameters of the multiple linear regression model M3 by using the method of to the lowest degree squares.

Table nine. Estimated parameters of the multiple linear regression model M3 by using the method of least squares.

Variable Coefficient Std. Error t-Statistic Prob.
C 5.173465 0.240976 21.46883 0.0000
log(TX1B1) 0.211263 0.052112 4.054010 0.0001
log(TX1C1) 0.372942 0.084366 4.420517 0.0000
log(TX1D1) 0.143356 0.084269 ane.701164 0.0942
log(TX1E1) 0.109920 0.051485 ii.134995 0.0369
R-squared 0.929221 Mean dependent var 11.61481
Adjusted R-squared 0.924422 Southward.D. dependent var 0.532504
Due south.E. of regression 0.146393 Akaike info criterion −0.930141
Sum squared resid i.264421 Schwarz criterion −0.761479
Log likelihood 34.76452 Hannan–Quinn criter. −0.863697
F-statistic 193.6448 Durbin–Watson stat one.378223
Prob(F-statistic) 0.000000 Wald F-statistic 433.4057
Prob(Wald F-statistic) 0.000000

Table 10. Estimated parameters of the multiple linear regression model M4 by using the method of least squares.

Table ten. Estimated parameters of the multiple linear regression model M4 past using the method of least squares.

Variable Coefficient Std. Error t-Statistic Prob.
C −0.572733 0.199315 −2.873508 0.0042
X1B2 0.000609 0.000151 four.020901 0.0001
log(X2B) 0.350974 0.047841 7.336311 0.0000
log(X3B) 0.507031 0.050006 x.13947 0.0000
R-squared 0.759192 Hateful dependent var 6.146454
Adjusted R-squared 0.758319 S.D. dependent var 1.689388
Southward.E. of regression 0.830521 Akaike info criterion 2.471268
Sum squared resid 571.1249 Schwarz benchmark ii.493979
Log likelihood −1024.047 Hannan–Quinn criter. ii.479976
F-statistic 870.1407 Durbin–Watson stat 0.371520
Prob(F-statistic) 0.000000 Wald F-statistic 538.1923
Prob(Wald F-statistic) 0.000000

Tabular array xi. Estimated parameters of the multiple linear regression model M5 by using the method of least squares.

Table 11. Estimated parameters of the multiple linear regression model M5 by using the method of least squares.

Variable Coefficient Std. Error t-Statistic Prob.
C 8.513681 0.645311 13.19313 0.0000
log(TX4D) −0.078573 0.075360 −1.042647 0.3090
log(TX4E) 0.369872 0.092168 4.013022 0.0006
R-squared 0.592076 Mean dependent var 11.70371
Adapted R-squared 0.553226 Due south.D. dependent var 0.520231
S.Due east. of regression 0.347729 Akaike info criterion 0.841681
Sum squared resid 2.539221 Schwarz criterion 0.988937
Log likelihood −7.100167 Hannan–Quinn criter. 0.880748
F-statistic 15.24007 Durbin–Watson stat 1.142976
Prob(F-statistic) 0.000081 Wald F-statistic 13.00972
Prob(Wald F-statistic) 0.000211

Table 12. Estimated parameters of the simple linear regression model M6 by using the method of to the lowest degree squares.

Table 12. Estimated parameters of the unproblematic linear regression model M6 by using the method of least squares.

Variable Coefficient Std. Mistake t-Statistic Prob.
C 8.564310 0.896656 9.551389 0.0000
log(TX4A) 0.256394 0.075632 3.390020 0.0026
R-squared 0.390104 Hateful dependent var xi.70371
Adjusted R-squared 0.362382 Southward.D. dependent var 0.520231
S.E. of regression 0.415410 Akaike info criterion 1.160553
Sum squared resid 3.796439 Schwarz criterion 1.258725
Log likelihood −eleven.92664 Hannan–Quinn criter. one.186598
F-statistic 14.07174 Durbin–Watson stat 1.684148
Prob(F-statistic) 0.001104 Wald F-statistic 11.49224
Prob(Wald F-statistic) 0.002633

Table 13. Estimated parameters of the unproblematic linear regression model M7 by using the method of least squares.

Tabular array 13. Estimated parameters of the simple linear regression model M7 by using the method of least squares.

Variable Coefficient Std. Error t-Statistic Prob.
C 8.292346 0.739660 11.21103 0.0000
log(TX4E) 0.306904 0.068674 iv.468970 0.0002
R-squared 0.574391 Mean dependent var 11.70371
Adjusted R-squared 0.555045 Southward.D. dependent var 0.520231
S.E. of regression 0.347020 Akaike info benchmark 0.800788
Sum squared resid two.649306 Schwarz benchmark 0.898959
Log likelihood −7.609451 Hannan–Quinn criter. 0.826832
F-statistic 29.69058 Durbin–Watson stat ane.083840
Prob(F-statistic) 0.000018 Wald F-statistic 19.97170
Prob(Wald F-statistic) 0.000192

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Source: https://www.mdpi.com/2071-1050/12/1/347/htm

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